Security risk assessment of projects in high-risk areas based on attack-defense game model.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
16 Aug 2023
Historique:
received: 26 05 2023
accepted: 09 08 2023
medline: 17 8 2023
pubmed: 17 8 2023
entrez: 16 8 2023
Statut: epublish

Résumé

Assessing the security risk of projects in high-risk areas is particularly important. This paper develops a security risk assessment model for projects in high-risk areas based on the target loss probability model and Bayesian game model. This model is modeled from the perspective of attack-defense confrontation and addresses the issue that traditional risk assessment focuses on the analysis of the attacker yet neglects to analyze the defender-the defender's optimum defensive information is not quantitatively determined. The risk level, optimum defensive resource value, and optimum defensive strategy of the project are determined through the analysis of a project in the high-risk area. This enables the project's risk manager to adjust the defensive resources reasonably and optimally, confirming the objectivity and feasibility of the model and offering a new benchmark for security risk assessment, which has significant practical implications.

Identifiants

pubmed: 37587177
doi: 10.1038/s41598-023-40409-w
pii: 10.1038/s41598-023-40409-w
pmc: PMC10432394
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

13293

Subventions

Organisme : the 2019 JKF227 Basic Theory Research on Security Prevention and the 2019 National Basic Research Business Fee Project
ID : Grant No. 2019 JKF227
Organisme : Basic research business fee support project
ID : Grant No. 2022 JKF02017

Informations de copyright

© 2023. Springer Nature Limited.

Références

Sci Total Environ. 2020 Mar 15;708:134436
pubmed: 31780148

Auteurs

Yifan Yao (Y)

Academy of Information and Network Security, People's Public Security University of China, Beijing, 100038, China.

Wenjing Chen (W)

Academy of Information and Network Security, People's Public Security University of China, Beijing, 100038, China. chenwenjing@ppsuc.edu.cn.

Classifications MeSH